Papers
Pure and Strong Nash Equilibrium Computation in Compactly Representable Aggregate Games
Jared Soundy, Mohammad T. Irfan, Hau Chan
RDI: An adversarial robustness evaluation metric for deep neural networks based on model statistical features
Jialei Song, Xingquan Zuo, Feiyang Wang et al.
Privacy-Preserving Neural Processes for Probabilistic User Modeling
Amir Sonee, Haripriya Harikumar, Alex Hämäläinen et al.
Proxy-informed Bayesian transfer learning with unknown sources
Sabina J. Sloman, Julien Martinelli, Samuel Kaski
Approximate Bayesian Inference via Bitstring Representations
Aleksanteri Sladek, Martin Trapp, Arno Solin
Learning from Label Proportions and Covariate-shifted Instances
Sagalpreet Singh, Navodita Sharma, Shreyas Havaldar et al.
Truthful Elicitation of Imprecise Forecasts
Anurag Singh, Siu Lun Chau, Krikamol Muandet
The Causal Information Bottleneck and Optimal Causal Variable Abstractions
Francisco N. F. Q. Simoes, Mehdi Dastani, Thijs van Ommen
Critical Influence of Overparameterization on Sharpness-aware Minimization
Sungbin Shin, Dongyeop Lee, Maksym Andriushchenko et al.
Minimax Optimal Nonsmooth Nonparametric Regression via Fractional Laplacian Eigenmaps
Zhaoyang Shi, Krishna Balasubramanian, Wolfgang Polonik
Learning Robust XGBoost Ensembles for Regression Tasks
Atri Vivek Sharma, Panagiotis Kouvaros, Alessio Lomuscio
Experimentation under Treatment Dependent Network Interference
Shiv Shankar, Ritwik Sinha, Madalina Fiterau
Divide and Orthogonalize: Efficient Continual Learning with Local Model Space Projection
Jin Shang, Simone Shao, Tian Tong et al.
Reparameterizing Hybrid Markov Logic Networks to handle Covariate-Shift in Representations
Anup Shakya, Abisha Thapa Magar, Somdeb Sarkhel et al.
Conformal Prediction Sets for Deep Generative Models via Reduction to Conformal Regression
Hooman Shahrokhi, Devjeet Raj Roy, Yan Yan et al.
Scaling Probabilistic Circuits via Data Partitioning
Jonas Seng, Florian Peter Busch, Pooja Prasad et al.
Revisiting the Equivalence of Bayesian Neural Networks and Gaussian Processes: On the Importance of Learning Activations
Marcin Sendera, Amin Sorkhei, Tomasz Kuśmierczyk
Distributionally and Adversarially Robust Logistic Regression via Intersecting Wasserstein Balls
Aras Selvi, Eleonora Kreacic, Mohsen Ghassemi et al.
On Information-Theoretic Measures of Predictive Uncertainty
Kajetan Schweighofer, Lukas Aichberger, Mykyta Ielanskyi et al.
Scalable Bayesian Low-Rank Adaptation of Large Language Models via Stochastic Variational Subspace Inference
Colin Samplawski, Adam D. Cobb, Manoj Acharya et al.
What is the Right Notion of Distance between Predict-then-Optimize Tasks?
Paula Rodriguez-Diaz, Lingkai Kong, Kai Wang et al.
Learning with Confidence
Oliver Ethan Richardson
COS-DPO: Conditioned One-Shot Multi-Objective Fine-Tuning Framework
Yinuo Ren, Tesi Xiao, Michael Shavlovsky et al.
FeDCM: Federated Learning of Deep Causal Generative Models
Md Musfiqur Rahman, Murat Kocaoglu
Enhanced Equilibria-Solving via Private Information Pre-Branch Structure in Adversarial Team Games
Chen Qiu, Haobo Fu, Kai Li et al.